
for the experimental data obtained from the fluorescence protein [1]}. In addition to these plots, we have attached videos as supplemental materials where we visualize the learning trajectory of a DNN initialized using Xavier initialization under data-scarce (red) and data-sufficient (blue) regimes. DNNs start from a local minima that does not have a well-structured WHT representation, and then gets sparser with training. However, if a sufficient amount of data is not available for training, the network does not converge to a good solution. Spectral regularisation enable DNN converge to the sparse solution in the data-scarce regime.
<FIGURE>